Objectives To study the impact of blinding on estimated treatment effects, and their variation between trials; differentiating between blinding of patients, healthcare providers, and observers; detection bias and performance bias; and types of outcome (the MetaBLIND study). Design Meta-epidemiological study. Data source Cochrane Database of Systematic Reviews (2013-14). Eligibility criteria for selecting studies Meta-analyses with both blinded and non-blinded trials on any topic. Review methods Blinding status was retrieved from trial publications and authors, and results retrieved automatically from the Cochrane Database of Systematic Reviews. Bayesian hierarchical models estimated the average ratio of odds ratios (ROR), and estimated the increases in heterogeneity between trials, for non-blinded trials (or of unclear status) versus blinded trials. Secondary analyses adjusted for adequacy of concealment of allocation, attrition, and trial size, and explored the association between outcome subjectivity (high, moderate, low) and average bias. An ROR lower than 1 indicated exaggerated effect estimates in trials without blinding. Results The study included 142 meta-analyses (1153 trials). The ROR for lack of blinding of patients was 0.91 (95% credible interval 0.61 to 1.34) in 18 meta-analyses with patient reported outcomes, and 0.98 (0.69 to 1.39) in 14 meta-analyses with outcomes reported by blinded observers. The ROR for lack of blinding of healthcare providers was 1.01 (0.84 to 1.19) in 29 meta-analyses with healthcare provider decision outcomes (eg, readmissions), and 0.97 (0.64 to 1.45) in 13 meta-analyses with outcomes reported by blinded patients or observers. The ROR for lack of blinding of observers was 1.01 (0.86 to 1.18) in 46 meta-analyses with subjective observer reported outcomes, with no clear impact of degree of subjectivity. Information was insufficient to determine whether lack of blinding was associated with increased heterogeneity between trials. The ROR for trials not reported as double blind versus those that were double blind was 1.02 (0.90 to 1.13) in 74 meta-analyses. Conclusion No evidence was found for an average difference in estimated treatment effect between trials with and without blinded patients, healthcare providers, or outcome assessors. These results could reflect that blinding is less important than often believed or meta-epidemiological study limitations, such as residual confounding or imprecision. At this stage, replication of this study is suggested and blinding should remain a methodological safeguard in trials.

ObjectiveTo synthesise evidence on the average bias and heterogeneity associated with reported methodological features of randomized trials.DesignSystematic review of meta-epidemiological studies.MethodsWe retrieved eligible studies included in a recent AHRQ-EPC review on this topic (latest search September 2012), and searched Ovid MEDLINE and Ovid EMBASE for studies indexed from Jan 2012-May 2015. Data were extracted by one author and verified by another. We combined estimates of average bias (e.g. ratio of odds ratios (ROR) or difference in standardised mean differences (dSMD)) in meta-analyses using the random-effects model. Analyses were stratified by type of outcome (“mortality” versus “other objective” versus “subjective”). Direction of effect was standardised so that ROR < 1 and dSMD < 0 denotes a larger intervention effect estimate in trials with an inadequate or unclear (versus adequate) characteristic.ResultsWe included 24 studies. The available evidence suggests that intervention effect estimates may be exaggerated in trials with inadequate/unclear (versus adequate) sequence generation (ROR 0.93, 95% CI 0.86 to 0.99; 7 studies) and allocation concealment (ROR 0.90, 95% CI 0.84 to 0.97; 7 studies). For these characteristics, the average bias appeared to be larger in trials of subjective outcomes compared with other objective outcomes. Also, intervention effects for subjective outcomes appear to be exaggerated in trials with lack of/unclear blinding of participants (versus blinding) (dSMD -0.37, 95% CI -0.77 to 0.04; 2 studies), lack of/unclear blinding of outcome assessors (ROR 0.64, 95% CI 0.43 to 0.96; 1 study) and lack of/unclear double blinding (ROR 0.77, 95% CI 0.61 to 0.93; 1 study). The influence of other characteristics (e.g. unblinded trial personnel, attrition) is unclear.ConclusionsCertain characteristics of randomized trials may exaggerate intervention effect estimates. The average bias appears to be greatest in trials of subjective outcomes. More research on several characteristics, particularly attrition and selective reporting, is needed.

http://www.controlled-trials.com , ISRCTN 23557269.

Randomized clinical trials underpin evidence‐based clinical practice, but flaws in their conduct may lead to biased estimates of intervention effects and hence invalid treatment recommendations. The main approach to the empirical study of bias is to collate a number of meta‐analyses and, within each, compare the results of trials with and without a methodological characteristic such as blinding of participants and health professionals. Estimated within‐meta‐analysis differences are combined across meta‐analyses, leading to an estimate of mean bias. Such “meta‐epidemiological” studies are published in increasing numbers and have the potential to inform trial design, assessment of risk of bias, and reporting guidelines. However, their interpretation is complicated by issues of confounding, imprecision, and applicability. We developed a guide for interpreting meta‐epidemiological studies, illustrated using MetaBLIND, a large study on the impact of blinding. Applying generally accepted principles of research methodology to meta‐epidemiology, we framed 10 questions covering the main issues to consider when interpreting results of such studies, including risk of systematic error, risk of random error, issues related to heterogeneity, and theoretical plausibility. We suggest that readers of a meta‐epidemiological study reflect comprehensively on the research question posed in the study, whether an experimental intervention was unequivocally identified for all included trials, the risk of misclassification of the trial characteristic, and the risk of confounding, i.e the adequacy of any adjustment for the likely confounders. We hope that our guide to interpretation of results of meta‐epidemiological studies is helpful for readers of such studies.